import json
import logging
import traceback
from typing import Any

from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.runnables import RunnableConfig
from langgraph.types import StreamWriter
from sqlalchemy.ext.asyncio import AsyncSession

# Additional imports for document fetching
from sqlalchemy.future import select

from app.db import Document, SearchSpace
from app.services.connector_service import ConnectorService
from app.services.query_service import QueryService

from .configuration import Configuration, SearchMode
from .prompts import get_further_questions_system_prompt
from .qna_agent.graph import graph as qna_agent_graph
from .state import State
from .utils import get_connector_emoji, get_connector_friendly_name


def extract_sources_from_documents(
    all_documents: list[dict[str, Any]],
) -> list[dict[str, Any]]:
    """
    Extract sources from all_documents and group them by document type.

    Args:
        all_documents: List of document chunks from user-selected documents and connector-fetched documents

    Returns:
        List of source objects grouped by type for streaming
    """
    # Group documents by their source type
    documents_by_type = {}

    for doc in all_documents:
        # Get source type from the document
        source_type = doc.get("source", "UNKNOWN")
        document_info = doc.get("document", {})
        document_type = document_info.get("document_type", source_type)

        # Use document_type if available, otherwise use source
        group_type = document_type if document_type != "UNKNOWN" else source_type

        if group_type not in documents_by_type:
            documents_by_type[group_type] = []
        documents_by_type[group_type].append(doc)

    # Create source objects for each document type
    source_objects = []
    source_id_counter = 1

    for doc_type, docs in documents_by_type.items():
        sources_list = []

        for doc in docs:
            document_info = doc.get("document", {})
            metadata = document_info.get("metadata", {})

            # Create source entry based on document structure
            source = {
                "id": doc.get("chunk_id", source_id_counter),
                "title": document_info.get("title", "Untitled Document"),
                "description": doc.get("content", "").strip(),
                "url": metadata.get("url", metadata.get("page_url", "")),
            }

            source_id_counter += 1
            sources_list.append(source)

        # Create group object
        group_name = (
            get_connector_friendly_name(doc_type)
            if doc_type != "UNKNOWN"
            else "Unknown Sources"
        )

        source_object = {
            "id": len(source_objects) + 1,
            "name": group_name,
            "type": doc_type,
            "sources": sources_list,
        }

        source_objects.append(source_object)

    return source_objects


async def fetch_documents_by_ids(
    document_ids: list[int], user_id: str, db_session: AsyncSession
) -> tuple[list[dict[str, Any]], list[dict[str, Any]]]:
    """
    Fetch documents by their IDs with ownership check using DOCUMENTS mode approach.

    This function ensures that only documents belonging to the user are fetched,
    providing security by checking ownership through SearchSpace association.
    Similar to SearchMode.DOCUMENTS, it fetches full documents and concatenates their chunks.
    Also creates source objects for UI display, grouped by document type.

    Args:
        document_ids: List of document IDs to fetch
        user_id: The user ID to check ownership
        db_session: The database session

    Returns:
        Tuple of (source_objects, document_chunks) - similar to ConnectorService pattern
    """
    if not document_ids:
        return [], []

    try:
        # Query documents with ownership check
        result = await db_session.execute(
            select(Document)
            .join(SearchSpace)
            .filter(Document.id.in_(document_ids), SearchSpace.user_id == user_id)
        )
        documents = result.scalars().all()

        # Group documents by type for source object creation
        documents_by_type = {}
        formatted_documents = []

        for doc in documents:
            # Fetch associated chunks for this document (similar to DocumentHybridSearchRetriever)
            from app.db import Chunk

            chunks_query = (
                select(Chunk).where(Chunk.document_id == doc.id).order_by(Chunk.id)
            )
            chunks_result = await db_session.execute(chunks_query)
            chunks = chunks_result.scalars().all()

            # Return individual chunks instead of concatenated content
            if chunks:
                for chunk in chunks:
                    # Format each chunk to match connector service return format
                    formatted_chunk = {
                        "chunk_id": chunk.id,
                        "content": chunk.content,  # Use individual chunk content
                        "score": 0.5,  # High score since user explicitly selected these
                        "document": {
                            "id": chunk.id,
                            "title": doc.title,
                            "document_type": (
                                doc.document_type.value
                                if doc.document_type
                                else "UNKNOWN"
                            ),
                            "metadata": doc.document_metadata or {},
                        },
                        "source": doc.document_type.value
                        if doc.document_type
                        else "UNKNOWN",
                    }
                    formatted_documents.append(formatted_chunk)

                    # Group by document type for source objects
                    doc_type = (
                        doc.document_type.value if doc.document_type else "UNKNOWN"
                    )
                    if doc_type not in documents_by_type:
                        documents_by_type[doc_type] = []
                    documents_by_type[doc_type].append(doc)

        # Create source objects for each document type (similar to ConnectorService)
        source_objects = []
        connector_id_counter = 100

        for doc_type, docs in documents_by_type.items():
            sources_list = []

            for doc in docs:
                metadata = doc.document_metadata or {}

                # Create type-specific source formatting (similar to ConnectorService)
                if doc_type == "LINEAR_CONNECTOR":
                    # Extract Linear-specific metadata
                    issue_identifier = metadata.get("issue_identifier", "")
                    issue_title = metadata.get("issue_title", doc.title)
                    issue_state = metadata.get("state", "")
                    comment_count = metadata.get("comment_count", 0)

                    # Create a more descriptive title for Linear issues
                    title = (
                        f"Linear: {issue_identifier} - {issue_title}"
                        if issue_identifier
                        else f"Linear: {issue_title}"
                    )
                    if issue_state:
                        title += f" ({issue_state})"

                    # Create description
                    description = doc.content
                    if comment_count:
                        description += f" | Comments: {comment_count}"

                    # Create URL
                    url = (
                        f"https://linear.app/issue/{issue_identifier}"
                        if issue_identifier
                        else ""
                    )

                elif doc_type == "SLACK_CONNECTOR":
                    # Extract Slack-specific metadata
                    channel_name = metadata.get("channel_name", "Unknown Channel")
                    channel_id = metadata.get("channel_id", "")
                    message_date = metadata.get("start_date", "")

                    title = f"Slack: {channel_name}"
                    if message_date:
                        title += f" ({message_date})"

                    description = doc.content
                    url = (
                        f"https://slack.com/app_redirect?channel={channel_id}"
                        if channel_id
                        else ""
                    )

                elif doc_type == "NOTION_CONNECTOR":
                    # Extract Notion-specific metadata
                    page_title = metadata.get("page_title", doc.title)
                    page_id = metadata.get("page_id", "")

                    title = f"Notion: {page_title}"
                    description = doc.content
                    url = (
                        f"https://notion.so/{page_id.replace('-', '')}"
                        if page_id
                        else ""
                    )

                elif doc_type == "GITHUB_CONNECTOR":
                    title = f"GitHub: {doc.title}"
                    description = metadata.get(
                        "description",
                        (doc.content),
                    )
                    url = metadata.get("url", "")

                elif doc_type == "YOUTUBE_VIDEO":
                    # Extract YouTube-specific metadata
                    video_title = metadata.get("video_title", doc.title)
                    video_id = metadata.get("video_id", "")
                    channel_name = metadata.get("channel_name", "")

                    title = video_title
                    if channel_name:
                        title += f" - {channel_name}"

                    description = metadata.get(
                        "description",
                        (doc.content),
                    )
                    url = (
                        f"https://www.youtube.com/watch?v={video_id}"
                        if video_id
                        else ""
                    )

                elif doc_type == "DISCORD_CONNECTOR":
                    # Extract Discord-specific metadata
                    channel_name = metadata.get("channel_name", "Unknown Channel")
                    channel_id = metadata.get("channel_id", "")
                    guild_id = metadata.get("guild_id", "")
                    message_date = metadata.get("start_date", "")

                    title = f"Discord: {channel_name}"
                    if message_date:
                        title += f" ({message_date})"

                    description = doc.content

                    if guild_id and channel_id:
                        url = f"https://discord.com/channels/{guild_id}/{channel_id}"
                    elif channel_id:
                        url = f"https://discord.com/channels/@me/{channel_id}"
                    else:
                        url = ""

                elif doc_type == "JIRA_CONNECTOR":
                    # Extract Jira-specific metadata
                    issue_key = metadata.get("issue_key", "Unknown Issue")
                    issue_title = metadata.get("issue_title", "Untitled Issue")
                    status = metadata.get("status", "")
                    priority = metadata.get("priority", "")
                    issue_type = metadata.get("issue_type", "")

                    title = f"Jira: {issue_key} - {issue_title}"
                    if status:
                        title += f" ({status})"

                    description = doc.content
                    if priority:
                        description += f" | Priority: {priority}"
                    if issue_type:
                        description += f" | Type: {issue_type}"

                    # Construct Jira URL if we have the base URL
                    base_url = metadata.get("base_url", "")
                    if base_url and issue_key:
                        url = f"{base_url}/browse/{issue_key}"
                    else:
                        url = ""

                elif doc_type == "GOOGLE_CALENDAR_CONNECTOR":
                    # Extract Google Calendar-specific metadata
                    event_id = metadata.get("event_id", "Unknown Event")
                    event_summary = metadata.get("event_summary", "Untitled Event")
                    calendar_id = metadata.get("calendar_id", "")
                    start_time = metadata.get("start_time", "")
                    location = metadata.get("location", "")

                    title = f"Calendar: {event_summary}"
                    if start_time:
                        # Format the start time for display
                        try:
                            if "T" in start_time:
                                from datetime import datetime

                                start_dt = datetime.fromisoformat(
                                    start_time.replace("Z", "+00:00")
                                )
                                formatted_time = start_dt.strftime("%Y-%m-%d %H:%M")
                                title += f" ({formatted_time})"
                            else:
                                title += f" ({start_time})"
                        except Exception:
                            title += f" ({start_time})"

                elif doc_type == "AIRTABLE_CONNECTOR":
                    # Extract Airtable-specific metadata
                    base_name = metadata.get("base_name", "Unknown Base")
                    table_name = metadata.get("table_name", "Unknown Table")
                    record_id = metadata.get("record_id", "Unknown Record")
                    created_time = metadata.get("created_time", "")

                    title = f"Airtable: {base_name} - {table_name}"
                    if record_id:
                        title += f" (Record: {record_id[:8]}...)"
                    if created_time:
                        # Format the created time for display
                        try:
                            if "T" in created_time:
                                from datetime import datetime

                                created_dt = datetime.fromisoformat(
                                    created_time.replace("Z", "+00:00")
                                )
                                formatted_time = created_dt.strftime("%Y-%m-%d %H:%M")
                                title += f" - {formatted_time}"
                        except Exception:
                            pass

                    description = doc.content
                    if location:
                        description += f" | Location: {location}"
                    if calendar_id and calendar_id != "primary":
                        description += f" | Calendar: {calendar_id}"

                    # Construct Google Calendar URL
                    if event_id:
                        url = (
                            f"https://calendar.google.com/calendar/event?eid={event_id}"
                        )
                    else:
                        url = ""

                elif doc_type == "LUMA_CONNECTOR":
                    # Extract Luma-specific metadata
                    event_id = metadata.get("event_id", "")
                    event_name = metadata.get("event_name", "Untitled Event")
                    event_url = metadata.get("event_url", "")
                    start_time = metadata.get("start_time", "")
                    location_name = metadata.get("location_name", "")
                    meeting_url = metadata.get("meeting_url", "")

                    title = f"Luma: {event_name}"
                    if start_time:
                        # Format the start time for display
                        try:
                            if "T" in start_time:
                                from datetime import datetime

                                start_dt = datetime.fromisoformat(
                                    start_time.replace("Z", "+00:00")
                                )
                                formatted_time = start_dt.strftime("%Y-%m-%d %H:%M")
                                title += f" ({formatted_time})"
                        except Exception:
                            pass

                    description = doc.content

                    if location_name:
                        description += f" | Venue: {location_name}"
                    elif meeting_url:
                        description += " | Online Event"

                    url = event_url if event_url else ""

                elif doc_type == "EXTENSION":
                    # Extract Extension-specific metadata
                    webpage_title = metadata.get("VisitedWebPageTitle", doc.title)
                    webpage_url = metadata.get("VisitedWebPageURL", "")
                    visit_date = metadata.get(
                        "VisitedWebPageDateWithTimeInISOString", ""
                    )

                    title = webpage_title
                    if visit_date:
                        formatted_date = (
                            visit_date.split("T")[0]
                            if "T" in visit_date
                            else visit_date
                        )
                        title += f" (visited: {formatted_date})"

                    description = doc.content
                    url = webpage_url

                elif doc_type == "CRAWLED_URL":
                    title = doc.title
                    description = metadata.get(
                        "og:description",
                        metadata.get(
                            "ogDescription",
                            (doc.content),
                        ),
                    )
                    url = metadata.get("url", "")

                elif doc_type == "ELASTICSEARCH_CONNECTOR":
                    # Prefer explicit title in metadata/source, otherwise fallback to doc.title
                    es_title = (
                        metadata.get("title")
                        or metadata.get("es_title")
                        or doc.title
                        or f"Elasticsearch: {metadata.get('elasticsearch_index', '')}"
                    )
                    title = es_title
                    description = metadata.get("description") or (
                        doc.content[:100] + "..."
                        if len(doc.content) > 100
                        else doc.content
                    )
                    # If a link or index info is stored, surface it
                    url = metadata.get("url", "") or metadata.get(
                        "elasticsearch_index", ""
                    )

                else:  # FILE and other types
                    title = doc.title
                    description = doc.content

                    url = metadata.get("url", "")

                # Create source entry
                source = {
                    "id": doc.id,
                    "title": title,
                    "description": description,
                    "url": url,
                }
                sources_list.append(source)

            # Create source object for this document type
            friendly_type_names = {
                "LINEAR_CONNECTOR": "Linear Issues (Selected)",
                "SLACK_CONNECTOR": "Slack (Selected)",
                "NOTION_CONNECTOR": "Notion (Selected)",
                "GITHUB_CONNECTOR": "GitHub (Selected)",
                "ELASTICSEARCH_CONNECTOR": "Elasticsearch (Selected)",
                "YOUTUBE_VIDEO": "YouTube Videos (Selected)",
                "DISCORD_CONNECTOR": "Discord (Selected)",
                "JIRA_CONNECTOR": "Jira Issues (Selected)",
                "EXTENSION": "Browser Extension (Selected)",
                "CRAWLED_URL": "Web Pages (Selected)",
                "FILE": "Files (Selected)",
                "GOOGLE_CALENDAR_CONNECTOR": "Google Calendar (Selected)",
                "GOOGLE_GMAIL_CONNECTOR": "Google Gmail (Selected)",
                "CONFLUENCE_CONNECTOR": "Confluence (Selected)",
                "CLICKUP_CONNECTOR": "ClickUp (Selected)",
                "AIRTABLE_CONNECTOR": "Airtable (Selected)",
                "LUMA_CONNECTOR": "Luma Events (Selected)",
            }

            source_object = {
                "id": connector_id_counter,
                "name": friendly_type_names.get(doc_type, f"{doc_type} (Selected)"),
                "type": f"USER_SELECTED_{doc_type}",
                "sources": sources_list,
            }
            source_objects.append(source_object)
            connector_id_counter += 1

        print(
            f"Fetched {len(formatted_documents)} user-selected chunks from {len(document_ids)} requested document IDs"
        )
        print(f"Created {len(source_objects)} source objects for UI display")

        return source_objects, formatted_documents

    except Exception as e:
        print(f"Error fetching documents by IDs: {e!s}")
        return [], []


async def fetch_relevant_documents(
    research_questions: list[str],
    user_id: str,
    search_space_id: int,
    db_session: AsyncSession,
    connectors_to_search: list[str],
    writer: StreamWriter = None,
    state: State = None,
    top_k: int = 10,
    connector_service: ConnectorService = None,
    search_mode: SearchMode = SearchMode.CHUNKS,
    user_selected_sources: list[dict[str, Any]] | None = None,
) -> list[dict[str, Any]]:
    """
    Fetch relevant documents for research questions using the provided connectors.

    This function searches across multiple data sources for information related to the
    research questions. It provides user-friendly feedback during the search process by
    displaying connector names (like "Web Search" instead of "TAVILY_API") and adding
    relevant emojis to indicate the type of source being searched.

    Args:
        research_questions: List of research questions to find documents for
        user_id: The user ID
        search_space_id: The search space ID
        db_session: The database session
        connectors_to_search: List of connectors to search
        writer: StreamWriter for sending progress updates
        state: The current state containing the streaming service
        top_k: Number of top results to retrieve per connector per question
        connector_service: An initialized connector service to use for searching

    Returns:
        List of relevant documents
    """
    # Initialize services
    # connector_service = ConnectorService(db_session)

    # Only use streaming if both writer and state are provided
    streaming_service = state.streaming_service if state is not None else None

    # Handle case when no connectors are selected
    if not connectors_to_search or len(connectors_to_search) == 0:
        if streaming_service and writer:
            writer(
                {
                    "yield_value": streaming_service.format_terminal_info_delta(
                        "📹 No data sources selected. Research will be generated using general knowledge and any user-selected documents."
                    )
                }
            )
        print("No connectors selected for research. Returning empty document list.")
        return []  # Return empty list gracefully

    # Stream initial status update
    if streaming_service and writer:
        connector_names = [
            get_connector_friendly_name(connector) for connector in connectors_to_search
        ]
        connector_names_str = ", ".join(connector_names)
        writer(
            {
                "yield_value": streaming_service.format_terminal_info_delta(
                    f"🔎 Starting research on {len(research_questions)} questions using {connector_names_str} data sources"
                )
            }
        )

    all_raw_documents = []  # Store all raw documents
    all_sources = []  # Store all sources

    for i, user_query in enumerate(research_questions):
        # Stream question being researched
        if streaming_service and writer:
            writer(
                {
                    "yield_value": streaming_service.format_terminal_info_delta(
                        f'🧠 Researching question {i + 1}/{len(research_questions)}: "{user_query[:100]}..."'
                    )
                }
            )

        # Use original research question as the query
        reformulated_query = user_query

        # Process each selected connector
        for connector in connectors_to_search:
            # Stream connector being searched
            if streaming_service and writer:
                connector_emoji = get_connector_emoji(connector)
                friendly_name = get_connector_friendly_name(connector)
                writer(
                    {
                        "yield_value": streaming_service.format_terminal_info_delta(
                            f"{connector_emoji} Searching {friendly_name} for relevant information..."
                        )
                    }
                )

            try:
                if connector == "YOUTUBE_VIDEO":
                    (
                        source_object,
                        youtube_chunks,
                    ) = await connector_service.search_youtube(
                        user_query=reformulated_query,
                        user_id=user_id,
                        search_space_id=search_space_id,
                        top_k=top_k,
                        search_mode=search_mode,
                    )

                    # Add to sources and raw documents
                    if source_object:
                        all_sources.append(source_object)
                    all_raw_documents.extend(youtube_chunks)

                    # Stream found document count
                    if streaming_service and writer:
                        writer(
                            {
                                "yield_value": streaming_service.format_terminal_info_delta(
                                    f"📹 Found {len(youtube_chunks)} YouTube chunks related to your query"
                                )
                            }
                        )

                elif connector == "EXTENSION":
                    (
                        source_object,
                        extension_chunks,
                    ) = await connector_service.search_extension(
                        user_query=reformulated_query,
                        user_id=user_id,
                        search_space_id=search_space_id,
                        top_k=top_k,
                        search_mode=search_mode,
                    )

                    # Add to sources and raw documents
                    if source_object:
                        all_sources.append(source_object)
                    all_raw_documents.extend(extension_chunks)

                    # Stream found document count
                    if streaming_service and writer:
                        writer(
                            {
                                "yield_value": streaming_service.format_terminal_info_delta(
                                    f"🧩 Found {len(extension_chunks)} Browser Extension chunks related to your query"
                                )
                            }
                        )

                elif connector == "CRAWLED_URL":
                    (
                        source_object,
                        crawled_urls_chunks,
                    ) = await connector_service.search_crawled_urls(
                        user_query=reformulated_query,
                        user_id=user_id,
                        search_space_id=search_space_id,
                        top_k=top_k,
                        search_mode=search_mode,
                    )

                    # Add to sources and raw documents
                    if source_object:
                        all_sources.append(source_object)
                    all_raw_documents.extend(crawled_urls_chunks)

                    # Stream found document count
                    if streaming_service and writer:
                        writer(
                            {
                                "yield_value": streaming_service.format_terminal_info_delta(
                                    f"🌐 Found {len(crawled_urls_chunks)} Web Pages chunks related to your query"
                                )
                            }
                        )

                elif connector == "FILE":
                    source_object, files_chunks = await connector_service.search_files(
                        user_query=reformulated_query,
                        user_id=user_id,
                        search_space_id=search_space_id,
                        top_k=top_k,
                        search_mode=search_mode,
                    )

                    # Add to sources and raw documents
                    if source_object:
                        all_sources.append(source_object)
                    all_raw_documents.extend(files_chunks)

                    # Stream found document count
                    if streaming_service and writer:
                        writer(
                            {
                                "yield_value": streaming_service.format_terminal_info_delta(
                                    f"📄 Found {len(files_chunks)} Files chunks related to your query"
                                )
                            }
                        )

                elif connector == "SLACK_CONNECTOR":
                    source_object, slack_chunks = await connector_service.search_slack(
                        user_query=reformulated_query,
                        user_id=user_id,
                        search_space_id=search_space_id,
                        top_k=top_k,
                        search_mode=search_mode,
                    )

                    # Add to sources and raw documents
                    if source_object:
                        all_sources.append(source_object)
                    all_raw_documents.extend(slack_chunks)

                    # Stream found document count
                    if streaming_service and writer:
                        writer(
                            {
                                "yield_value": streaming_service.format_terminal_info_delta(
                                    f"💬 Found {len(slack_chunks)} Slack messages related to your query"
                                )
                            }
                        )

                elif connector == "NOTION_CONNECTOR":
                    (
                        source_object,
                        notion_chunks,
                    ) = await connector_service.search_notion(
                        user_query=reformulated_query,
                        user_id=user_id,
                        search_space_id=search_space_id,
                        top_k=top_k,
                        search_mode=search_mode,
                    )

                    # Add to sources and raw documents
                    if source_object:
                        all_sources.append(source_object)
                    all_raw_documents.extend(notion_chunks)

                    # Stream found document count
                    if streaming_service and writer:
                        writer(
                            {
                                "yield_value": streaming_service.format_terminal_info_delta(
                                    f"📘 Found {len(notion_chunks)} Notion pages/blocks related to your query"
                                )
                            }
                        )

                elif connector == "GITHUB_CONNECTOR":
                    (
                        source_object,
                        github_chunks,
                    ) = await connector_service.search_github(
                        user_query=reformulated_query,
                        user_id=user_id,
                        search_space_id=search_space_id,
                        top_k=top_k,
                        search_mode=search_mode,
                    )

                    # Add to sources and raw documents
                    if source_object:
                        all_sources.append(source_object)
                    all_raw_documents.extend(github_chunks)

                    # Stream found document count
                    if streaming_service and writer:
                        writer(
                            {
                                "yield_value": streaming_service.format_terminal_info_delta(
                                    f"🐙 Found {len(github_chunks)} GitHub files/issues related to your query"
                                )
                            }
                        )

                elif connector == "LINEAR_CONNECTOR":
                    (
                        source_object,
                        linear_chunks,
                    ) = await connector_service.search_linear(
                        user_query=reformulated_query,
                        user_id=user_id,
                        search_space_id=search_space_id,
                        top_k=top_k,
                        search_mode=search_mode,
                    )

                    # Add to sources and raw documents
                    if source_object:
                        all_sources.append(source_object)
                    all_raw_documents.extend(linear_chunks)

                    # Stream found document count
                    if streaming_service and writer:
                        writer(
                            {
                                "yield_value": streaming_service.format_terminal_info_delta(
                                    f"📊 Found {len(linear_chunks)} Linear issues related to your query"
                                )
                            }
                        )

                elif connector == "TAVILY_API":
                    (
                        source_object,
                        tavily_chunks,
                    ) = await connector_service.search_tavily(
                        user_query=reformulated_query,
                        user_id=user_id,
                        search_space_id=search_space_id,
                        top_k=top_k,
                    )

                    # Add to sources and raw documents
                    if source_object:
                        all_sources.append(source_object)
                    all_raw_documents.extend(tavily_chunks)

                    # Stream found document count
                    if streaming_service and writer:
                        writer(
                            {
                                "yield_value": streaming_service.format_terminal_info_delta(
                                    f"🔍 Found {len(tavily_chunks)} Web Search results related to your query"
                                )
                            }
                        )

                elif connector == "SEARXNG_API":
                    (
                        source_object,
                        searx_chunks,
                    ) = await connector_service.search_searxng(
                        user_query=reformulated_query,
                        user_id=user_id,
                        search_space_id=search_space_id,
                        top_k=top_k,
                    )

                    if source_object:
                        all_sources.append(source_object)
                    all_raw_documents.extend(searx_chunks)

                    if streaming_service and writer:
                        writer(
                            {
                                "yield_value": streaming_service.format_terminal_info_delta(
                                    f"🌐 Found {len(searx_chunks)} SearxNG results related to your query"
                                )
                            }
                        )

                elif connector == "LINKUP_API":
                    linkup_mode = "standard"

                    (
                        source_object,
                        linkup_chunks,
                    ) = await connector_service.search_linkup(
                        user_query=reformulated_query,
                        user_id=user_id,
                        search_space_id=search_space_id,
                        mode=linkup_mode,
                    )

                    # Add to sources and raw documents
                    if source_object:
                        all_sources.append(source_object)
                    all_raw_documents.extend(linkup_chunks)

                    # Stream found document count
                    if streaming_service and writer:
                        writer(
                            {
                                "yield_value": streaming_service.format_terminal_info_delta(
                                    f"🔗 Found {len(linkup_chunks)} Linkup results related to your query"
                                )
                            }
                        )

                elif connector == "BAIDU_SEARCH_API":
                    (
                        source_object,
                        baidu_chunks,
                    ) = await connector_service.search_baidu(
                        user_query=reformulated_query,
                        user_id=user_id,
                        search_space_id=search_space_id,
                        top_k=top_k,
                    )

                    # Add to sources and raw documents
                    if source_object:
                        all_sources.append(source_object)
                    all_raw_documents.extend(baidu_chunks)

                    # Stream found document count
                    if streaming_service and writer:
                        writer(
                            {
                                "yield_value": streaming_service.format_terminal_info_delta(
                                    f"🇨🇳 Found {len(baidu_chunks)} Baidu Search results related to your query"
                                )
                            }
                        )

                elif connector == "DISCORD_CONNECTOR":
                    (
                        source_object,
                        discord_chunks,
                    ) = await connector_service.search_discord(
                        user_query=reformulated_query,
                        user_id=user_id,
                        search_space_id=search_space_id,
                        top_k=top_k,
                        search_mode=search_mode,
                    )
                    # Add to sources and raw documents
                    if source_object:
                        all_sources.append(source_object)
                    all_raw_documents.extend(discord_chunks)
                    # Stream found document count
                    if streaming_service and writer:
                        writer(
                            {
                                "yield_value": streaming_service.format_terminal_info_delta(
                                    f"🗨️ Found {len(discord_chunks)} Discord messages related to your query"
                                )
                            }
                        )

                elif connector == "JIRA_CONNECTOR":
                    source_object, jira_chunks = await connector_service.search_jira(
                        user_query=reformulated_query,
                        user_id=user_id,
                        search_space_id=search_space_id,
                        top_k=top_k,
                        search_mode=search_mode,
                    )

                    # Add to sources and raw documents
                    if source_object:
                        all_sources.append(source_object)
                    all_raw_documents.extend(jira_chunks)

                    # Stream found document count
                    if streaming_service and writer:
                        writer(
                            {
                                "yield_value": streaming_service.format_terminal_info_delta(
                                    f"🎫 Found {len(jira_chunks)} Jira issues related to your query"
                                )
                            }
                        )
                elif connector == "GOOGLE_CALENDAR_CONNECTOR":
                    (
                        source_object,
                        calendar_chunks,
                    ) = await connector_service.search_google_calendar(
                        user_query=reformulated_query,
                        user_id=user_id,
                        search_space_id=search_space_id,
                        top_k=top_k,
                        search_mode=search_mode,
                    )

                    # Add to sources and raw documents
                    if source_object:
                        all_sources.append(source_object)
                    all_raw_documents.extend(calendar_chunks)

                    # Stream found document count
                    if streaming_service and writer:
                        writer(
                            {
                                "yield_value": streaming_service.format_terminal_info_delta(
                                    f"📅 Found {len(calendar_chunks)} calendar events related to your query"
                                )
                            }
                        )
                elif connector == "AIRTABLE_CONNECTOR":
                    (
                        source_object,
                        airtable_chunks,
                    ) = await connector_service.search_airtable(
                        user_query=reformulated_query,
                        user_id=user_id,
                        search_space_id=search_space_id,
                        top_k=top_k,
                        search_mode=search_mode,
                    )

                    # Add to sources and raw documents
                    if source_object:
                        all_sources.append(source_object)
                    all_raw_documents.extend(airtable_chunks)

                    # Stream found document count
                    if streaming_service and writer:
                        writer(
                            {
                                "yield_value": streaming_service.format_terminal_info_delta(
                                    f"🗃️ Found {len(airtable_chunks)} Airtable records related to your query"
                                )
                            }
                        )
                elif connector == "GOOGLE_GMAIL_CONNECTOR":
                    (
                        source_object,
                        gmail_chunks,
                    ) = await connector_service.search_google_gmail(
                        user_query=reformulated_query,
                        user_id=user_id,
                        search_space_id=search_space_id,
                        top_k=top_k,
                        search_mode=search_mode,
                    )

                    # Add to sources and raw documents
                    if source_object:
                        all_sources.append(source_object)
                    all_raw_documents.extend(gmail_chunks)

                    # Stream found document count
                    if streaming_service and writer:
                        writer(
                            {
                                "yield_value": streaming_service.format_terminal_info_delta(
                                    f"📧 Found {len(gmail_chunks)} Gmail messages related to your query"
                                )
                            }
                        )
                elif connector == "CONFLUENCE_CONNECTOR":
                    (
                        source_object,
                        confluence_chunks,
                    ) = await connector_service.search_confluence(
                        user_query=reformulated_query,
                        user_id=user_id,
                        search_space_id=search_space_id,
                        top_k=top_k,
                        search_mode=search_mode,
                    )

                    # Add to sources and raw documents
                    if source_object:
                        all_sources.append(source_object)
                    all_raw_documents.extend(confluence_chunks)

                    # Stream found document count
                    if streaming_service and writer:
                        writer(
                            {
                                "yield_value": streaming_service.format_terminal_info_delta(
                                    f"📚 Found {len(confluence_chunks)} Confluence pages related to your query"
                                )
                            }
                        )
                elif connector == "CLICKUP_CONNECTOR":
                    (
                        source_object,
                        clickup_chunks,
                    ) = await connector_service.search_clickup(
                        user_query=reformulated_query,
                        user_id=user_id,
                        search_space_id=search_space_id,
                        top_k=top_k,
                        search_mode=search_mode,
                    )

                    # Add to sources and raw documents
                    if source_object:
                        all_sources.append(source_object)
                    all_raw_documents.extend(clickup_chunks)

                    # Stream found document count
                    if streaming_service and writer:
                        writer(
                            {
                                "yield_value": streaming_service.format_terminal_info_delta(
                                    f"📋 Found {len(clickup_chunks)} ClickUp tasks related to your query"
                                )
                            }
                        )

                elif connector == "LUMA_CONNECTOR":
                    (
                        source_object,
                        luma_chunks,
                    ) = await connector_service.search_luma(
                        user_query=reformulated_query,
                        user_id=user_id,
                        search_space_id=search_space_id,
                        top_k=top_k,
                        search_mode=search_mode,
                    )

                    # Add to sources and raw documents
                    if source_object:
                        all_sources.append(source_object)
                    all_raw_documents.extend(luma_chunks)

                    # Stream found document count
                    if streaming_service and writer:
                        writer(
                            {
                                "yield_value": streaming_service.format_terminal_info_delta(
                                    f"🎯 Found {len(luma_chunks)} Luma events related to your query"
                                )
                            }
                        )

                elif connector == "ELASTICSEARCH_CONNECTOR":
                    (
                        source_object,
                        elasticsearch_chunks,
                    ) = await connector_service.search_elasticsearch(
                        user_query=reformulated_query,
                        user_id=user_id,
                        search_space_id=search_space_id,
                        top_k=top_k,
                        search_mode=search_mode,
                    )

                    # Add to sources and raw documents
                    if source_object:
                        all_sources.append(source_object)
                    all_raw_documents.extend(elasticsearch_chunks)

                    # Stream found document count
                    if streaming_service and writer:
                        writer(
                            {
                                "yield_value": streaming_service.format_terminal_info_delta(
                                    f"🔎 Found {len(elasticsearch_chunks)} Elasticsearch chunks related to your query"
                                )
                            }
                        )

            except Exception as e:
                logging.error("Error in search_airtable: %s", traceback.format_exc())
                error_message = f"Error searching connector {connector}: {e!s}"
                print(error_message)

                # Stream error message
                if streaming_service and writer:
                    friendly_name = get_connector_friendly_name(connector)
                    writer(
                        {
                            "yield_value": streaming_service.format_error(
                                f"Error searching {friendly_name}: {e!s}"
                            )
                        }
                    )

                # Continue with other connectors on error
                continue

    # Deduplicate source objects by ID before streaming
    deduplicated_sources = []
    seen_source_keys = set()

    # First add user-selected sources (if any)
    if user_selected_sources:
        for source_obj in user_selected_sources:
            source_id = source_obj.get("id")
            source_type = source_obj.get("type")

            if source_id and source_type:
                source_key = f"{source_type}_{source_id}"
                if source_key not in seen_source_keys:
                    seen_source_keys.add(source_key)
                    deduplicated_sources.append(source_obj)
            else:
                deduplicated_sources.append(source_obj)

    # Then add connector sources
    for source_obj in all_sources:
        # Use combination of source ID and type as a unique identifier
        # This ensures we don't accidentally deduplicate sources from different connectors
        source_id = source_obj.get("id")
        source_type = source_obj.get("type")

        if source_id and source_type:
            source_key = f"{source_type}_{source_id}"
            current_sources_count = len(source_obj.get("sources", []))

            if source_key not in seen_source_keys:
                seen_source_keys.add(source_key)
                deduplicated_sources.append(source_obj)
                print(
                    f"Debug: Added source - ID: {source_id}, Type: {source_type}, Key: {source_key}, Sources count: {current_sources_count}"
                )
            else:
                # Check if this source object has more sources than the existing one
                existing_index = None
                for i, existing_source in enumerate(deduplicated_sources):
                    existing_id = existing_source.get("id")
                    existing_type = existing_source.get("type")
                    if existing_id == source_id and existing_type == source_type:
                        existing_index = i
                        break

                if existing_index is not None:
                    existing_sources_count = len(
                        deduplicated_sources[existing_index].get("sources", [])
                    )
                    if current_sources_count > existing_sources_count:
                        # Replace the existing source object with the new one that has more sources
                        deduplicated_sources[existing_index] = source_obj
                        print(
                            f"Debug: Replaced source - ID: {source_id}, Type: {source_type}, Key: {source_key}, Sources count: {existing_sources_count} -> {current_sources_count}"
                        )
                    else:
                        print(
                            f"Debug: Skipped duplicate source - ID: {source_id}, Type: {source_type}, Key: {source_key}, Sources count: {current_sources_count} <= {existing_sources_count}"
                        )
                else:
                    print(
                        f"Debug: Skipped duplicate source - ID: {source_id}, Type: {source_type}, Key: {source_key} (couldn't find existing)"
                    )
        else:
            # If there's no ID or type, just add it to be safe
            deduplicated_sources.append(source_obj)
            print(
                f"Debug: Added source without ID/type - {source_obj.get('name', 'UNKNOWN')}"
            )

    # Stream info about deduplicated sources
    if streaming_service and writer:
        user_source_count = len(user_selected_sources) if user_selected_sources else 0
        connector_source_count = len(deduplicated_sources) - user_source_count
        writer(
            {
                "yield_value": streaming_service.format_terminal_info_delta(
                    f"📚 Collected {len(deduplicated_sources)} total sources ({user_source_count} user-selected + {connector_source_count} from connectors)"
                )
            }
        )

    # Deduplicate raw documents based on chunk_id or content
    seen_chunk_ids = set()
    seen_content_hashes = set()
    deduplicated_docs = []

    for doc in all_raw_documents:
        chunk_id = doc.get("chunk_id")
        content = doc.get("content", "")
        content_hash = hash(content)

        # Skip if we've seen this chunk_id or content before
        if (
            chunk_id and chunk_id in seen_chunk_ids
        ) or content_hash in seen_content_hashes:
            continue

        # Add to our tracking sets and keep this document
        if chunk_id:
            seen_chunk_ids.add(chunk_id)
        seen_content_hashes.add(content_hash)
        deduplicated_docs.append(doc)

    # Stream info about deduplicated documents
    if streaming_service and writer:
        writer(
            {
                "yield_value": streaming_service.format_terminal_info_delta(
                    f"🧹 Found {len(deduplicated_docs)} unique document chunks after removing duplicates"
                )
            }
        )

    # Return deduplicated documents
    return deduplicated_docs


async def reformulate_user_query(
    state: State, config: RunnableConfig, writer: StreamWriter
) -> dict[str, Any]:
    """
    Reforms the user query based on the chat history.
    """

    configuration = Configuration.from_runnable_config(config)
    user_query = configuration.user_query
    chat_history_str = await QueryService.langchain_chat_history_to_str(
        state.chat_history
    )
    if len(state.chat_history) == 0:
        reformulated_query = user_query
    else:
        reformulated_query = await QueryService.reformulate_query_with_chat_history(
            user_query=user_query,
            session=state.db_session,
            user_id=configuration.user_id,
            search_space_id=configuration.search_space_id,
            chat_history_str=chat_history_str,
        )

    return {"reformulated_query": reformulated_query}


async def handle_qna_workflow(
    state: State, config: RunnableConfig, writer: StreamWriter
) -> dict[str, Any]:
    """
    Handle the QNA research workflow.

    This node fetches relevant documents for the user query and then uses the QNA agent
    to generate a comprehensive answer with proper citations.

    Returns:
        Dict containing the final answer in the "final_written_report" key for consistency.
    """
    streaming_service = state.streaming_service
    configuration = Configuration.from_runnable_config(config)

    reformulated_query = state.reformulated_query
    user_query = configuration.user_query

    writer(
        {
            "yield_value": streaming_service.format_terminal_info_delta(
                "🤔 Starting Q&A research workflow..."
            )
        }
    )

    writer(
        {
            "yield_value": streaming_service.format_terminal_info_delta(
                f'🔍 Researching: "{user_query[:100]}..."'
            )
        }
    )

    # Fetch relevant documents for the QNA query
    writer(
        {
            "yield_value": streaming_service.format_terminal_info_delta(
                "🔍 Searching for relevant information across all connectors..."
            )
        }
    )

    # Use the top_k value from configuration
    top_k = configuration.top_k

    relevant_documents = []
    user_selected_documents = []
    user_selected_sources = []

    try:
        # First, fetch user-selected documents if any
        if configuration.document_ids_to_add_in_context:
            writer(
                {
                    "yield_value": streaming_service.format_terminal_info_delta(
                        f"📋 Including {len(configuration.document_ids_to_add_in_context)} user-selected documents..."
                    )
                }
            )

            (
                user_selected_sources,
                user_selected_documents,
            ) = await fetch_documents_by_ids(
                document_ids=configuration.document_ids_to_add_in_context,
                user_id=configuration.user_id,
                db_session=state.db_session,
            )

            if user_selected_documents:
                writer(
                    {
                        "yield_value": streaming_service.format_terminal_info_delta(
                            f"✅ Successfully added {len(user_selected_documents)} user-selected documents to context"
                        )
                    }
                )

        # Create connector service using state db_session
        connector_service = ConnectorService(
            state.db_session, user_id=configuration.user_id
        )
        await connector_service.initialize_counter()

        # Use the reformulated query as a single research question
        research_questions = [reformulated_query, user_query]

        relevant_documents = await fetch_relevant_documents(
            research_questions=research_questions,
            user_id=configuration.user_id,
            search_space_id=configuration.search_space_id,
            db_session=state.db_session,
            connectors_to_search=configuration.connectors_to_search,
            writer=writer,
            state=state,
            top_k=top_k,
            connector_service=connector_service,
            search_mode=configuration.search_mode,
            user_selected_sources=user_selected_sources,
        )
    except Exception as e:
        error_message = f"Error fetching relevant documents for QNA: {e!s}"
        print(error_message)
        writer({"yield_value": streaming_service.format_error(error_message)})
        # Continue with empty documents - the QNA agent will handle this gracefully
        relevant_documents = []

    # Combine user-selected documents with connector-fetched documents
    all_documents = user_selected_documents + relevant_documents

    print(f"Fetched {len(relevant_documents)} relevant documents for QNA")
    print(f"Added {len(user_selected_documents)} user-selected documents for QNA")
    print(f"Total documents for QNA: {len(all_documents)}")

    # Extract and stream sources from all_documents
    if all_documents:
        sources_to_stream = extract_sources_from_documents(all_documents)
        writer(
            {"yield_value": streaming_service.format_sources_delta(sources_to_stream)}
        )

    writer(
        {
            "yield_value": streaming_service.format_terminal_info_delta(
                f"🧠 Generating comprehensive answer using {len(all_documents)} total sources ({len(user_selected_documents)} user-selected + {len(relevant_documents)} connector-found)..."
            )
        }
    )

    # Prepare configuration for the QNA agent
    qna_config = {
        "configurable": {
            "user_query": user_query,  # Use the reformulated query
            "reformulated_query": reformulated_query,
            "relevant_documents": all_documents,  # Use combined documents
            "user_id": configuration.user_id,
            "search_space_id": configuration.search_space_id,
            "language": configuration.language,
        }
    }

    # Create the state for the QNA agent (it has a different state structure)
    qna_state = {"db_session": state.db_session, "chat_history": state.chat_history}

    try:
        writer(
            {
                "yield_value": streaming_service.format_terminal_info_delta(
                    "✍️ Writing comprehensive answer ..."
                )
            }
        )

        # Track streaming content for real-time updates
        complete_content = ""
        captured_reranked_documents = []

        # Call the QNA agent with streaming
        async for _chunk_type, chunk in qna_agent_graph.astream(
            qna_state, qna_config, stream_mode=["values"]
        ):
            if "final_answer" in chunk:
                new_content = chunk["final_answer"]
                if new_content and new_content != complete_content:
                    # Extract only the new content (delta)
                    delta = new_content[len(complete_content) :]
                    complete_content = new_content

                    # Stream the real-time answer if there's new content
                    if delta:
                        # Update terminal with progress
                        word_count = len(complete_content.split())
                        writer(
                            {
                                "yield_value": streaming_service.format_terminal_info_delta(
                                    f"✍️ Writing answer... ({word_count} words)"
                                )
                            }
                        )

                        writer(
                            {"yield_value": streaming_service.format_text_chunk(delta)}
                        )

            # Capture reranked documents from QNA agent for further question generation
            if "reranked_documents" in chunk:
                captured_reranked_documents = chunk["reranked_documents"]

        # Set default if no content was received
        if not complete_content:
            complete_content = "I couldn't find relevant information in your knowledge base to answer this question."

        writer(
            {
                "yield_value": streaming_service.format_terminal_info_delta(
                    "🎉 Q&A answer generated successfully!"
                )
            }
        )

        # Return the final answer and captured reranked documents for further question generation
        return {
            "final_written_report": complete_content,
            "reranked_documents": captured_reranked_documents,
        }

    except Exception as e:
        error_message = f"Error generating QNA answer: {e!s}"
        print(error_message)
        writer({"yield_value": streaming_service.format_error(error_message)})

        return {"final_written_report": f"Error generating answer: {e!s}"}


async def generate_further_questions(
    state: State, config: RunnableConfig, writer: StreamWriter
) -> dict[str, Any]:
    """
    Generate contextually relevant follow-up questions based on chat history and available documents.

    This node takes the chat history and reranked documents from the QNA agent
    and uses an LLM to generate follow-up questions that would naturally extend the conversation
    and provide additional value to the user.

    Returns:
        Dict containing the further questions in the "further_questions" key for state update.
    """
    from app.services.llm_service import get_user_fast_llm

    # Get configuration and state data
    configuration = Configuration.from_runnable_config(config)
    chat_history = state.chat_history
    user_id = configuration.user_id
    search_space_id = configuration.search_space_id
    streaming_service = state.streaming_service

    # Get reranked documents from the state (will be populated by sub-agents)
    reranked_documents = getattr(state, "reranked_documents", None) or []

    writer(
        {
            "yield_value": streaming_service.format_terminal_info_delta(
                "🤔 Generating follow-up questions..."
            )
        }
    )

    # Get user's fast LLM
    llm = await get_user_fast_llm(state.db_session, user_id, search_space_id)
    if not llm:
        error_message = f"No fast LLM configured for user {user_id} in search space {search_space_id}"
        print(error_message)
        writer({"yield_value": streaming_service.format_error(error_message)})

        # Stream empty further questions to UI
        writer({"yield_value": streaming_service.format_further_questions_delta([])})
        return {"further_questions": []}

    # Format chat history for the prompt
    chat_history_xml = "<chat_history>\n"
    for message in chat_history:
        if hasattr(message, "type"):
            if message.type == "human":
                chat_history_xml += f"<user>{message.content}</user>\n"
            elif message.type == "ai":
                chat_history_xml += f"<assistant>{message.content}</assistant>\n"
        else:
            # Handle other message types if needed
            chat_history_xml += f"<message>{message!s}</message>\n"
    chat_history_xml += "</chat_history>"

    # Format available documents for the prompt
    documents_xml = "<documents>\n"
    for i, doc in enumerate(reranked_documents):
        document_info = doc.get("document", {})
        source_id = document_info.get("id", f"doc_{i}")
        source_type = document_info.get("document_type", "UNKNOWN")
        content = doc.get("content", "")

        documents_xml += "<document>\n"
        documents_xml += "<metadata>\n"
        documents_xml += f"<source_id>{source_id}</source_id>\n"
        documents_xml += f"<source_type>{source_type}</source_type>\n"
        documents_xml += "</metadata>\n"
        documents_xml += f"<content>\n{content}</content>\n"
        documents_xml += "</document>\n"
    documents_xml += "</documents>"

    # Create the human message content
    human_message_content = f"""
    {chat_history_xml}

    {documents_xml}

    Based on the chat history and available documents above, generate 3-5 contextually relevant follow-up questions that would naturally extend the conversation and provide additional value to the user. Make sure the questions can be reasonably answered using the available documents or knowledge base.

    Your response MUST be valid JSON in exactly this format:
    {{
      "further_questions": [
        {{
          "id": 0,
          "question": "further qn 1"
        }},
        {{
          "id": 1,
          "question": "further qn 2"
        }}
      ]
    }}

    Do not include any other text or explanation. Only return the JSON.
    """

    writer(
        {
            "yield_value": streaming_service.format_terminal_info_delta(
                "🧠 Analyzing conversation context to suggest relevant questions..."
            )
        }
    )

    # Create messages for the LLM
    messages = [
        SystemMessage(content=get_further_questions_system_prompt()),
        HumanMessage(content=human_message_content),
    ]

    try:
        # Call the LLM
        response = await llm.ainvoke(messages)

        # Parse the JSON response
        content = response.content

        # Find the JSON in the content
        json_start = content.find("{")
        json_end = content.rfind("}") + 1
        if json_start >= 0 and json_end > json_start:
            json_str = content[json_start:json_end]

            # Parse the JSON string
            parsed_data = json.loads(json_str)

            # Extract the further_questions array
            further_questions = parsed_data.get("further_questions", [])

            writer(
                {
                    "yield_value": streaming_service.format_terminal_info_delta(
                        f"✅ Generated {len(further_questions)} contextual follow-up questions!"
                    )
                }
            )

            # Stream the further questions to the UI
            writer(
                {
                    "yield_value": streaming_service.format_further_questions_delta(
                        further_questions
                    )
                }
            )

            print(f"Successfully generated {len(further_questions)} further questions")

            return {"further_questions": further_questions}
        else:
            # If JSON structure not found, return empty list
            error_message = (
                "Could not find valid JSON in LLM response for further questions"
            )
            print(error_message)
            writer(
                {
                    "yield_value": streaming_service.format_error(
                        f"Warning: {error_message}"
                    )
                }
            )

            # Stream empty further questions to UI
            writer(
                {"yield_value": streaming_service.format_further_questions_delta([])}
            )
            return {"further_questions": []}

    except (json.JSONDecodeError, ValueError) as e:
        # Log the error and return empty list
        error_message = f"Error parsing further questions response: {e!s}"
        print(error_message)
        writer(
            {"yield_value": streaming_service.format_error(f"Warning: {error_message}")}
        )

        # Stream empty further questions to UI
        writer({"yield_value": streaming_service.format_further_questions_delta([])})
        return {"further_questions": []}

    except Exception as e:
        # Handle any other errors
        error_message = f"Error generating further questions: {e!s}"
        print(error_message)
        writer(
            {"yield_value": streaming_service.format_error(f"Warning: {error_message}")}
        )

        # Stream empty further questions to UI
        writer({"yield_value": streaming_service.format_further_questions_delta([])})
        return {"further_questions": []}
